Flexible correction of 3D non-linear drift in SPM measurements by data fusion

Self Cite

We present a new method for traceable calibration of size and form error of microspheres, which was realised by stitching a series of atomic force microscopic (AFM) images measured at different orientations of microspheres using the metrological large range AFM of the PTB. The stitching algorithm is achieved using an iterative closest point (ICP) point-to-plane algorithm. As the AFM tip geometry is one of the most significant error sources for the developed method, it was traceably calibrated to a line width standard (type IVPS100-PTB), whose feature geometry was calibrated with a traceable route to the lattice constant of crystal silicon. Measurement setup, scan strategy, and data evaluation processes have been detailed in the paper. Measurement results show high stability and robustness of the developed method. For instance, the standard deviation of four repeated measurements reaches 5 nm, indicating promising performance.

Section: Measurement and Data Evaluation Proceduresmentioning

confidence: 99%

“…Consequently, drifts will inevitably distort measured images, leading to measurement errors. For mitigating the impact of drifts, online and offline drift correction methods can be applied [30]. As the Met.…”

Section: Measurement and Data Evaluation Proceduresmentioning

confidence: 99%

A feasibility study towards traceable calibration of size and form of microspheres by stitching AFM images using ICP point-to-plane algorithm

Dai

Degenhardt²,

Hu³

et al. 2023

Self Cite

“…The system uses the fast library for approximate nearest neighbors (FLANN) [20] algorithm optimized by the K nearest neighbor (KNN) [21] algorithm to improve the accuracy of feature matching. Then the system integrates EPnP [22] and iterative closest point (ICP) [23] algorithms to make full use of the best data obtained, which improves the accuracy of pose estimation. After loop detection and backend optimization, the system reconstructs the spatial model according to the optimized trajectory and the corresponding 3D point cloud.…”

Section: Introductionmentioning

confidence: 99%

A dense RGB-D SLAM algorithm based on convolutional neural network of multi-layer image invariant feature

Su¹,

Yu²

2021

Simultaneous Localization and Mapping (SLAM) is one of the key technologies used in sweepers, autonomous vehicles, virtual reality and other fields. This paper presents a dense RGB-D SLAM reconstruction algorithm based on convolutional neural network of multi-layer image invariant feature transformation. The main contribution of the system lies in the construction of a convolutional neural network based on multi-layer image invariant feature, which optimized the extraction of ORB (Oriented FAST and Rotated Brief) feature points and the reconstruction effect. After the feature point matching, pose estimation, loop detection and other steps, the 3D point clouds were finally spliced to construct a complete and smooth spatial model. The system can improve the accuracy and robustness in feature point processing and pose estimation. Comparative experiments show that the optimized algorithm saves 0.093s compared to the ordinary extraction algorithm while guaranteeing a high accuracy rate at the same time. The results of reconstruction experiments show that the spatial models have more clear details, smoother connection with no fault layers than the original ones. The reconstruction results are generally better than other common algorithms, such as Kintinuous, Elasticfusion and ORBSLAM2 dense reconstruction.

“…Three-dimensional drift correction routines for SPMs have been proposed by several authors [6][7][8][9]. The method used by Lapshin [6] is based on pairs of counter-scanned images and topography feature recognition, which enables the coefficients for the linear transformation to be determined for the correction of linear 3D drift.…”

Section: Introductionmentioning

confidence: 99%

“…These 3D drift correction methods all require significant additional scanning and thus extra measurement time with limited temporal drift resolution. Degenhardt et al [9] recently presented a method for 3D drift correction based on the measurement of a sample region in multiple sub-measurements, which are aligned using a point-to-plane iterative closest point algorithm to reconstruct and correct the 3D drift. Whilst this method has significantly reduced the data redundancy and improved temporal drift resolution, it used complex splitting of measurement at pixel levels and also complex point alignment of datasets in 3D, which can increase the overall measurement time.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional drift correction of scan data from atomic force microscopy using Lissajous scanning paths

Sun¹,

Heaps²,

Yacoot³

et al. 2021

Non-raster scanning is being widely used to increase the scanning speed of atomic force microscopes (AFMs). However, like any other AFM scanning techniques, non-raster scanning can also suffer from drift during the scanning process. This paper presents a simple novel method based on cross-correlation for the effective drift correction of non-raster Lissajous AFM scans with intersecting paths. The drift in x, y and z axes can be determined using the crossing points and repeated scans of the same features. In this paper, the general method is presented together with experimental results using Lissajous scans of two artifacts, which demonstrate that the drift in the three axes and the additional tilt can all be corrected resulting in significant improvement in the image obtained. Our drift correction method can overcome the limitations of the existing AFM drift correction methods which are usually based on extra scanning, and it exploits the crossing scan paths and four scanning cycles inherent in Lissajous scanning without using additional scans to effectively determine the three-dimensional drift using cross-correlation and least squares techniques. The drift correction method can be used with any AFMs capable of Lissajous scanning.